






'''simple docstring'''







import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      LayoutLMvaConfig,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
  )
  from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor




class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__=2   ,	UpperCamelCase__=3   ,	UpperCamelCase__=4   ,	UpperCamelCase__=2   ,	UpperCamelCase__=7   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=99   ,	UpperCamelCase__=36   ,	UpperCamelCase__=3   ,	UpperCamelCase__=4   ,	UpperCamelCase__=37   ,	UpperCamelCase__="gelu"   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=512   ,	UpperCamelCase__=16   ,	UpperCamelCase__=2   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=6   ,	UpperCamelCase__=6   ,	UpperCamelCase__=3   ,	UpperCamelCase__=4   ,	UpperCamelCase__=None   ,	UpperCamelCase__=1_000   ,	):






         """simple docstring"""


         a_						     =			parent
         a_						     =			batch_size
         a_						     =			num_channels
         a_						     =			image_size
         a_						     =			patch_size
         a_						     =			text_seq_length
         a_						     =			is_training
         a_						     =			use_input_mask
         a_						     =			use_token_type_ids
         a_						     =			use_labels
         a_						     =			vocab_size
         a_						     =			hidden_size
         a_						     =			num_hidden_layers
         a_						     =			num_attention_heads
         a_						     =			intermediate_size
         a_						     =			hidden_act
         a_						     =			hidden_dropout_prob
         a_						     =			attention_probs_dropout_prob
         a_						     =			max_position_embeddings
         a_						     =			type_vocab_size
         a_						     =			type_sequence_label_size
         a_						     =			initializer_range
         a_						     =			coordinate_size
         a_						     =			shape_size
         a_						     =			num_labels
         a_						     =			num_choices
         a_						     =			scope
         a_						     =			range_bbox

         # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
         a_						     =			text_seq_length
         a_						     =			(image_size // patch_size) ** 2 + 1
         a_						     =			self.text_seq_length + self.image_seq_length







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			ids_tensor([self.batch_size, self.text_seq_length]   ,	self.vocab_size	)

         a_						     =			ids_tensor([self.batch_size, self.text_seq_length, 4]   ,	self.range_bbox	)
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]	):
                for j in range(bbox.shape[1]	):
                       if bbox[i, j, 3] < bbox[i, j, 1]:
                              a_						     =			bbox[i, j, 3]
                              a_						     =			bbox[i, j, 1]
                              a_						     =			t
                       if bbox[i, j, 2] < bbox[i, j, 0]:
                              a_						     =			bbox[i, j, 2]
                              a_						     =			bbox[i, j, 0]
                              a_						     =			t

         a_						     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         a_						     =			None
         if self.use_input_mask:
                a_						     =			random_attention_mask([self.batch_size, self.text_seq_length]	)

         a_						     =			None
         if self.use_token_type_ids:
                a_						     =			ids_tensor([self.batch_size, self.text_seq_length]   ,	self.type_vocab_size	)

         a_						     =			None
         a_						     =			None
         if self.use_labels:
                a_						     =			ids_tensor([self.batch_size]   ,	self.type_sequence_label_size	)
                a_						     =			ids_tensor([self.batch_size, self.text_seq_length]   ,	self.num_labels	)

         a_						     =			LayoutLMvaConfig(
             vocab_size=self.vocab_size   ,	hidden_size=self.hidden_size   ,	num_hidden_layers=self.num_hidden_layers   ,	num_attention_heads=self.num_attention_heads   ,	intermediate_size=self.intermediate_size   ,	hidden_act=self.hidden_act   ,	hidden_dropout_prob=self.hidden_dropout_prob   ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,	max_position_embeddings=self.max_position_embeddings   ,	type_vocab_size=self.type_vocab_size   ,	initializer_range=self.initializer_range   ,	coordinate_size=self.coordinate_size   ,	shape_size=self.shape_size   ,	input_size=self.image_size   ,	patch_size=self.patch_size   ,	)

         return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			LayoutLMvaModel(config=UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()

         # text + image
         a_						     =			model(UpperCamelCase__   ,	pixel_values=UpperCamelCase__	)
         a_						     =			model(
             UpperCamelCase__   ,	bbox=UpperCamelCase__   ,	pixel_values=UpperCamelCase__   ,	attention_mask=UpperCamelCase__   ,	token_type_ids=UpperCamelCase__	)
         a_						     =			model(UpperCamelCase__   ,	bbox=UpperCamelCase__   ,	pixel_values=UpperCamelCase__   ,	token_type_ids=UpperCamelCase__	)
         a_						     =			model(UpperCamelCase__   ,	bbox=UpperCamelCase__   ,	pixel_values=UpperCamelCase__	)

         self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)	)

         # text only
         a_						     =			model(UpperCamelCase__	)
         self.parent.assertEqual(
             result.last_hidden_state.shape   ,	(self.batch_size, self.text_seq_length, self.hidden_size)	)

         # image only
         a_						     =			model(pixel_values=UpperCamelCase__	)
         self.parent.assertEqual(
             result.last_hidden_state.shape   ,	(self.batch_size, self.image_seq_length, self.hidden_size)	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			self.num_labels
         a_						     =			LayoutLMvaForSequenceClassification(UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(
             UpperCamelCase__   ,	bbox=UpperCamelCase__   ,	pixel_values=UpperCamelCase__   ,	attention_mask=UpperCamelCase__   ,	token_type_ids=UpperCamelCase__   ,	labels=UpperCamelCase__   ,	)
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			self.num_labels
         a_						     =			LayoutLMvaForTokenClassification(config=UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(
             UpperCamelCase__   ,	bbox=UpperCamelCase__   ,	pixel_values=UpperCamelCase__   ,	attention_mask=UpperCamelCase__   ,	token_type_ids=UpperCamelCase__   ,	labels=UpperCamelCase__   ,	)
         self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.text_seq_length, self.num_labels)	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			LayoutLMvaForQuestionAnswering(config=UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         model.eval()
         a_						     =			model(
             UpperCamelCase__   ,	bbox=UpperCamelCase__   ,	pixel_values=UpperCamelCase__   ,	attention_mask=UpperCamelCase__   ,	token_type_ids=UpperCamelCase__   ,	start_positions=UpperCamelCase__   ,	end_positions=UpperCamelCase__   ,	)
         self.parent.assertEqual(result.start_logits.shape   ,	(self.batch_size, self.seq_length)	)
         self.parent.assertEqual(result.end_logits.shape   ,	(self.batch_size, self.seq_length)	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.prepare_config_and_inputs()
         (
             (
             a_
         )     ,	(
             a_
         )     ,	(
             a_
         )     ,	(
             a_
         )     ,	(
             a_
         )     ,	(
             a_
         )     ,	(
             a_
         )     ,	(
             a_
         )     ,	
         )						     =			config_and_inputs
         a_						     =			{
             'input_ids': input_ids,
             'bbox': bbox,
             'pixel_values': pixel_values,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict





@require_torch
class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Optional[Any]      			=     False
  _a  : Dict      			=     False
  _a  : Any      			=     False

  _a  : Optional[int]      			=     (
      (
          LayoutLMvaModel,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  _a  : Any      			=     (
      {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
      if is_torch_available()
      else {}
  )







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         return True







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			LayoutLMvaModelTester(self	)
         a_						     =			ConfigTester(self   ,	config_class=UpperCamelCase__   ,	hidden_size=37	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__=False	):






         """simple docstring"""


         a_						     =			copy.deepcopy(UpperCamelCase__	)
         if model_class in get_values(UpperCamelCase__	):
                a_						     =			{
                    k: v.unsqueeze(1	).expand(-1   ,	self.model_tester.num_choices   ,	-1	).contiguous()
                    if isinstance(UpperCamelCase__   ,	torch.Tensor	) and v.ndim > 1
                    else v
                    for k, v in inputs_dict.items()
                }
         if return_labels:
                if model_class in get_values(UpperCamelCase__	):
                       a_						     =			torch.ones(self.model_tester.batch_size   ,	dtype=torch.long   ,	device=UpperCamelCase__	)
                elif model_class in get_values(UpperCamelCase__	):
                       a_						     =			torch.zeros(
                           self.model_tester.batch_size   ,	dtype=torch.long   ,	device=UpperCamelCase__	)
                       a_						     =			torch.zeros(
                           self.model_tester.batch_size   ,	dtype=torch.long   ,	device=UpperCamelCase__	)
                elif model_class in [
                    *get_values(UpperCamelCase__	),
                ]:
                       a_						     =			torch.zeros(
                           self.model_tester.batch_size   ,	dtype=torch.long   ,	device=UpperCamelCase__	)
                elif model_class in [
                    *get_values(UpperCamelCase__	),
                ]:
                       a_						     =			torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,	dtype=torch.long   ,	device=UpperCamelCase__   ,	)

         return inputs_dict







  def    _a       (			self	):






         """simple docstring"""


         self.config_tester.run_common_tests()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
                a_						     =			type
                self.model_tester.create_and_check_model(*UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__	)







  @slow
  def    _a       (			self	):






         """simple docstring"""


         for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_						     =			LayoutLMvaModel.from_pretrained(UpperCamelCase__	)
                self.assertIsNotNone(UpperCamelCase__	)





def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
       return image




@require_torch
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  @cached_property
  def    _a       (			self	):






         """simple docstring"""


         return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__	) if is_vision_available() else None







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'	).to(UpperCamelCase__	)

         a_						     =			self.default_image_processor
         a_						     =			prepare_img()
         a_						     =			image_processor(images=UpperCamelCase__   ,	return_tensors='pt'	).pixel_values.to(UpperCamelCase__	)

         a_						     =			torch.tensor([[1, 2]]	)
         a_						     =			torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]	).unsqueeze(0	)

         # forward pass
         a_						     =			model(
             input_ids=input_ids.to(UpperCamelCase__	)   ,	bbox=bbox.to(UpperCamelCase__	)   ,	pixel_values=pixel_values.to(UpperCamelCase__	)   ,	)

         # verify the logits
         a_						     =			torch.Size((1, 199, 768)	)
         self.assertEqual(outputs.last_hidden_state.shape   ,	UpperCamelCase__	)

         a_						     =			torch.tensor(
             [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]	).to(UpperCamelCase__	)

         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,	UpperCamelCase__   ,	atol=1e-4	)	)












'''simple docstring'''







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCAmelCase        =							abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)


def       __UpperCamelCase					(      lowercase_       :  Dict   ):
       """simple docstring"""




       from transformers.testing_utils import pytest_addoption_shared

       pytest_addoption_shared(lowercase_   )


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       from transformers.testing_utils import pytest_terminal_summary_main

       a_						     =			terminalreporter.config.getoption('--make-reports'   )
       if make_reports:
              pytest_terminal_summary_main(lowercase_   ,       id=lowercase_   )




